超密集异构网络中过载MEC服务器的协作卸载

doi:10.19665/j.issn1001-2400.2020.02.017

西安电子科技大学学报 ›› 2020, Vol. 47 ›› Issue (2): 126-134.doi: 10.19665/j.issn1001-2400.2020.02.017

超密集异构网络中过载MEC服务器的协作卸载

王忍^1,²,王翊^1,²(),胡艳军¹,蒋芳¹,许耀华¹

1.安徽大学计算智能与信号处理教育部重点实验室,安徽合肥 230601
2.中国科学院上海微系统与信息技术研究所无线传感网与通信重点实验室,上海 200050

收稿日期:2019-07-30 出版日期:2020-04-20 发布日期:2020-04-26
通讯作者: 王翊
作者简介:王忍(1992—),女,安徽大学硕士研究生,E-mail:952175687@qq.com
基金资助:
安徽省高校自然科学研究重大项目(KJ2017ZD03);安徽省高校自然科学研究重点项目(KJ2018A0019);中国科学院上海微系统与信息技术研究所无线传感网与通信重点实验室开放课题(20190911)

Collaborative offloading of overloaded MEC servers in ultra-dense heterogeneous networks

WANG Ren^1,²,WANG Yi^1,²(),HU Yanjun¹,JIANG Fang¹,XU Yaohua¹

1.Ministry of Education Key Laboratory of Intelligent Computing & Signal Processing,Anhui University, Hefei 230601, China
2.Key Laboratory of Wireless Sensor Network & Communication, Shanghai Institute of Microsystem and Information Technology, Chinese Academy of Sciences, Shanghai 200050, China

Received:2019-07-30 Online:2020-04-20 Published:2020-04-26
Contact: Yi WANG

摘要/Abstract

摘要：

由于移动边缘计算可通过借助边缘计算服务器实现计算任务卸载,不再受限于移动终端的计算能力, 所以当边缘服务器过载时,往往会选择排队、推迟或拒绝移动终端的业务请求。但现有研究大都没有考虑此时服务器如何缓解负载压力,由于服务中断和等待延长,用户的服务质量将明显恶化。研究了将过载移动边缘计算服务器的任务卸载到与其同一协作区间的其他服务器来提高计算卸载能力。将罚函数与两步拟牛顿法相结合给出了优化方法,联合优化包含边缘计算网络总时延和能耗的联合效用函数。针对优化目标对时延或者能效的不同需要,使用经验因子灵活调整时延和能量消耗最小化之间的优化偏差。仿真结果表明,卸载优化方法在提高关于时延和能耗的系统性能方面优于现有的两种比较方法,且以更快的速度收敛。

关键词: 移动边缘计算, 过载, 异构网络, 协作卸载, 联合优化

Abstract:

Mobile Edge Computing (MEC) can perform computational task offloading with the help of edge servers, and is no longer limited by the power of mobile terminals (MTs). When the edge server is overloaded, it often chooses to queue, postpone or reject the MT’s offloading request. QoS (Quality of Service) of users will deteriorate greatly due to service disruption and extended waiting, but the existing research work does not consider how the MEC-BS can relieve load pressure at this time. In this paper, we study how to enhance the computing offloading service of the MEC-BS by offloading the task of the overloaded base station to the other MEC-BS in the same collaboration space. Combining the penalty function with the two-step quasi-newton method, an optimization algorithm is proposed to minimize the joint utility function including the total delay and energy consumption of the edge computing network. Empirical factors are used to adjust the optimization deviation according to the different needs of the optimization target for time delay or energy efficiency. Simulation results show that the proposed scheme is better than two other schemes in improving the system performance and convergence speed.

Key words: mobile edge computing, overload, heterogeneous networks, collaborative offloading, joint optimization

中图分类号:

TN929.5

王忍,王翊,胡艳军,蒋芳,许耀华. 超密集异构网络中过载MEC服务器的协作卸载[J]. 西安电子科技大学学报, 2020, 47(2): 126-134.

WANG Ren,WANG Yi,HU Yanjun,JIANG Fang,XU Yaohua. Collaborative offloading of overloaded MEC servers in ultra-dense heterogeneous networks[J]. Journal of Xidian University, 2020, 47(2): 126-134.

图/表 9

图1

表1

仿真参数表"

参数	设置
小基站数量	[50, 150]
用户数量	[500, 1500]
宏小区半径/m	600
天线增益因子	1
p_i_,_j	[0, 1]
宏基站、小基站发射功率w_k, w₀/W	[35, 70], [5, 15]
$r i m, b$ , $r i b, b$ , $r i b, B$ /(MB·s^-1)	[1, 10], [10, 15] , [15, 20]
损耗因子	2
文件大小c_j/GB	[0, 20]
时延、能耗经验因子ε_t、ε_e	ε_t∈[0, 1],ε_e∈[0, 1]且ε_t+ε_e=1

表1

图2

图3

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超密集异构网络中过载MEC服务器的协作卸载

Collaborative offloading of overloaded MEC servers in ultra-dense heterogeneous networks

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